Bug Fixes
Route reproducible bugs into ticket-to-code runs with validation before review.
Turn Approved Tickets Into Review-Ready PRs
MergeLoom routes approved tickets through whole-system context, validation gates, audit trails, and pull requests your developers review.
Approved Tickets In. Review-Ready PRs/MRs Out. Engineers Still Control The Merge.
Approved Work Item
Tickets, bugs, features, and epic tasks from the tools your team already uses.
Ticket-To-Code Run
Context Engine maps repositories, APIs, docs, rules, and system relationships.
Quality Agents
Scope checks, validation, repairs, specialist review, Diff Guard, and run evidence.
Review-Ready PR/MR
Engineers review the pull request or merge request in GitHub, GitLab, or Azure DevOps. Nothing auto-merges.
Intake Sources
Output Destinations
Move Approved Work Out Of Senior Review Queues
Route approved tasks straight from Jira, Linear, GitHub, or wherever you track work. MergeLoom handles maintenance, bugs, tests, docs, features, and epic workstreams before handing back a validated PR/MR.
Keep The Workflow:
Your team keeps the same tickets, code host, validation gates, approval rules, and merge control. MergeLoom handles the repetitive coding loop in the middle.
Feature Work
Ship scoped feature tickets as review-ready PRs without pulling seniors into the first pass.
Epic Workstreams
Break approved epic work into smaller coding runs your team reviews in safer slices.
Tests And Coverage
Generate and validate useful test updates where pass/fail signals are clear.
Documentation
Keep technical docs, API notes, and code comments moving without blocking engineers.
Maintenance Runs
Move config updates, dependency chores, and routine cleanup out of the backlog.
From Ticket To PR/MR
Every run follows the same controlled path, so the work is repeatable, reviewable, and tied back to the original request.
- STEP 01
Ticket Approved
A status, label, assignment, or query marks work as ready in Jira, Linear, GitHub Issues, GitLab Issues, Monday, or Azure Boards.
- STEP 02
Whole-System Context Loads
Context Engine pulls the right repositories, docs, shared schemas, APIs, service dependencies, and architecture rules before code is written.
- STEP 03
Agents Write And Check
MergeLoom implements the change, runs configured commands, attempts bounded repairs, and reviews the diff from several angles.
- STEP 04
PR/MR Opens For Review
The finished branch lands in your normal code host with validation results, cost telemetry, and audit evidence attached.
- STEP 05
Engineers Control The Merge
Your team keeps branch protection, approval rules, release control, and final engineering judgement.
Agents Produce The Diff. Your Team Controls The Merge.
MergeLoom does the repetitive coding loop and only hands engineers cleaner PRs/MRs for review. You keep the approval rules that already protect production.
- Review-Ready PRs On Autopilot
- Engineers Keep Total Merge Control
- Your Team Dictates The Workflow Rules
- Full Data Receipts Attached To Every Run
- Zero Changes To Your Current Workflow
The Hidden Cost Of AI Coding At Scale
AI coding gets expensive when work starts in isolated prompts and ends in messy review. MergeLoom keeps the request, context, checks, cost, and output tied together.
No Clear Scope, No Reliable Output
Random AI chats start from fuzzy intent. Ticket-to-code starts from approved work with acceptance criteria, workflow rules, and a clear handoff target.
No Context, No System Fit
Single-window prompting misses related services, APIs, shared packages, and architecture decisions. MergeLoom gives each run whole-system context.
No Checks, More Review Waste
Broken commands and bloated diffs do not belong with senior reviewers. Quality Agents run validation and repairs before handoff.
No Cost Link, No ROI
Token bills are hard to defend when they are detached from output. MergeLoom ties spend to the ticket, run, validation status, and PR/MR.
What Happens Inside A Run
MergeLoom combines whole-system context, Quality Agents, and run evidence so AI-generated output reaches review cleaner and easier to trust.
Context Engine
Maps the working repository against related repositories, APIs, shared schemas, docs, rules, dependencies, and prior evidence before execution starts.
Quality Agents
Run ticket clarity, investigation, implementation, validation gates, repair, specialist review, Diff Guard, and final PR/MR checks before handoff.
Audit Trails
Track what changed, why it ran, which context was used, which checks passed, what it cost, and where the PR/MR landed.
Engineering Teams Are Already Moving Development Work To AI Agents
Stripe Engineering has publicly shared how its internal coding agents produce over 1,300 human-reviewed PRs per week. MergeLoom gives teams an AI coding workflow without building an internal agent platform from scratch.
Source: Stripe Engineering1,300+
PRs / Week Reported By Stripe
100%
Human-Reviewed PRs/MRs
0
Auto-Merged Code
2
Deployment Options
Do Not Get Left Behind.
Bring Down The Cost Of Approved Work
Repeatable runs stop AI spend disappearing into private debugging loops. Every run connects context overhead, validation status, telemetry, and PR/MR output.
Manual Engineering Work
~£100
Absorbed Run Cost
~£6-£7
Potential Reduction
90%+
Best Fit
Approved Tickets And Epics
Typical ranges. Unit economics vary by ticket type, repository complexity, validation depth, and model selection.
Cloud Hosted Or Self Hosted
Zero security compromises, two ways to deploy. Go Cloud for zero maintenance, or Self Hosted to keep sensitive execution inside your own perimeter.
Cloud Hosted
Zero maintenance. MergeLoom runs the worker path with tenant isolation, validation gates, audit logs, and managed AI provider usage.
Self Hosted
Run worker execution inside your VPC perimeter. Keep code checkout, credentials, tests, provider calls, traces, and local audit evidence under your control.
Common Questions
Straight answers for teams comparing AI coding assistants, PR bots, and controlled coding workflows.
What is ticket-to-code automation?
Ticket-to-code automation turns approved tickets into code changes. MergeLoom routes the ticket through context loading, implementation, validation, repair, review, and pull request or merge request handoff so engineers review the result instead of starting from a blank branch.
Does MergeLoom replace developers?
No. MergeLoom handles repetitive coding runs and cleanup. Engineers still decide what work is allowed, review every PR/MR, approve the merge, and own release decisions.
What counts as a billable ticket-to-code run?
A billable run is a run that opens a finished PR/MR for review. If no PR/MR is opened, there is no run charge.
Does ticket-to-code handle features and epic workstreams?
Yes. MergeLoom handles bugs, maintenance, tests, documentation, feature tickets, and epic workstreams split into reviewable tasks.
Which tools does MergeLoom work with?
MergeLoom is built around existing engineering workflows. It routes approved work from trackers like Jira, Linear, GitHub Issues, GitLab Issues, Monday, and Azure Boards, then publishes PRs or MRs into GitHub, GitLab, and Azure DevOps Repositories.
How is this different from asking an AI coding assistant to write code?
A chat assistant usually depends on whoever writes the prompt. MergeLoom turns ticket to code through a controlled run with whole-system context, validation gates, bounded repair, audit evidence, and human merge control.